Short Grammar Questions (Part 1)

Thank you for this incredibly well researched post. I think it has answered some questions and raised some others, but I guess keigo is just really, really complicated. :sweat_smile:

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ところがそう簡単に見つかりませんし、運良く見つけても使い方が分からなくて洗濯するのに何時間もかかったりして「手で洗った方が早かったということもありました。

Why is this being put in 「」?

For context she’s talking about looking for laundromats in foreign countries.

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Usually 「」are for direct quotes, just like “ “.

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…yes

But in this specific sentence why are we doing this and what does it mean? I didn’t mean to ask what 「」 is, I meant to ask why she’s putting “it was faster to be washed by hand” in quotes to begin with…

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In this case I’d say it’s because she literally heard someone say that, as opposed to a general “people say X” sense which ということ can imply.

Edit: So the sense is that even if she found one, she didn’t know how to use it well and it took so long, she could (literally) say “It was faster to wash by hand”

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Japanese is pretty loose with punctuation. You could have it or not and it wouldn’t change anything here. It’s not like this is meant to be metaphorical or emphasized specifically.

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Hi everyone, not sure if I mixed up multiple grammar points which are still new to me, or if this is just one I haven’t encountered yet. Would appreciate some help.

JRの「そうだ、京都行こう」の広告を見て京都に行きたくなったが、そんなお金はなかった。
The JR ad “That’s it! Let’s go to Kyoto.” made me want to go to Kyoto, but I didn’t have that kind of money.

To me it sounded like it should mean “didn’t want to go to Kyoto” because 行きたくなかった in my mind was deciphered as 行く + たい + negative + past.
Is this a case like 何か飲みませんか。where negative is actually positive for some reason?

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=/=

Does this help?

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Yes! Thank you!

Damn なる + い-adjectives though. Second time it got me.

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That construction comes up quite a bit. You might see くなる with い-adjectives , negations and the たい form expressing personal wants.

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I’ll watch out for the sneaky 〜くなる. True to your warning, it came up again already. Wasn’t fooled this time. Ha! Thanks!

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Quick question about 承る. I just learned the word via WaniKani with the example sentence:

ご伝言を承りましょうか。
Would you like to leave a message?

Looking for more examples online, this is like one of the three situations I could ever find the word used in. I’m struggling to make a connection between the definition and the translation I see, but my guess is the English translation is just creating an equivalent expression in English each time?

Literally, does it say, “Shall I hear a message (from you to pass on to a third party)?”

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This entry from Goo is useful I think: 承る(うけたまわる)とは? 意味・読み方・使い方をわかりやすく解説 - goo国語辞書

It’s the equivalent of お受けする or 拝聴する so the “me” humble (謙譲語) side of a conversation in keigo. I think a maybe slightly more natural translation would be Shall I take/pass your message?

The English version in WaniKani cuts the redirection typical to keigo.

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手作りスイーツ、ほっ。。。

Quick question. This is a line on the cover of a book I recently got, and I was wondering if this is connective だ or particle で? “Handmade sweets and (they are) a relief” doesn’t sound too good, “Relief by handmade sweets” is a little better, but “Handmade sweets are a relief” (from DeepL) sounds the best! BUT, how the hell does either connective だ or particle で turn into “are”???

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Remember that DeepL aims for natural-sounding sentences so sometimes it takes a little bit more liberty :wink: .

I think in this case it would be the regular だ switched to で, cause スイーツ is a noun. So kind of like “I’m relieved, because there are handmade sweets”. Is there anything else or is this the full sentence?

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It’s just like a little ‘tag line’ or w/e you call it on the cover. The title of the book is おいしいお茶、いれよ. Also, could you explain how だ turns to ‘because’? I’ve never seen that before!

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It doesn’t :smiley: . The translation I suggested wasn’t a literal one. However, sometimes when you connect two sentence clauses with て or で, it implies there is some relationship between them.

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I’ve seen that more naturally translated as “so,” which implies causality without being as direct as “because.”

I agree that it’s more likely the conjunctive だ than the で particle, mostly due to the comma (it being a noun has no special bearing, imo. You mark nouns with で particle all the time), but if somebody gave me “because” as a translation, I would jump to the causal で particle before the conjunctive で copula, personally. :stuck_out_tongue:

This almost feels like one of those situations where the uses are so similar that it’s a bit hard to judge which it is with 100% certainty (since it’s only a sentence fragment with no other context, really), but it’s not really necessary, either. Just being aware that the で implies causality, whether more directly as the particle, or less directly as the conjunctive is enough.

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DeepL works in mysterious ways. Seriously, I’m not even joking. Whatever logic there is there, it has everything to do with how the machine learning model is trained and less to do with what the most natural or even correct translation is. A lot of the times, and especially for Japanese, DeepL feels like it’s trying to fit a square block in a round hole. Sure, the result often looks coherent, but the meaning sometimes gets completely lost. The less you rely on it for language learning, the better, in my opinion.

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To be more specific, translation models are built from huge corpora of bilingual texts, e.g. sets of (for example) Japanese texts and their English translations. Therefore, they become quite good at plausible translations, because they can build up connections between Japanese text passages and how they’re usually translated in English.

This means that the translation is always going to prioritize natural sounding English over a literal translation - which often is what you want, but it will not be able to convey all the nuances necessarily.

Furthermore, such models are a bit limited compared to human translators in that humans don’t only establish connections between pairs of expressions (a trained translator probably has those too), but they can also connect language to real-world phenomena, something which is generally not accessible to language models.

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